Multichannel audio shows correlation across channels (e.g., wherein “channel” as used herein refers to a channel by one of the sequences in a multi-dimensional source signal). Removing the correlation can be beneficial to compression, noise suppression, and source separation. For example, removing the correlation reduces the redundancy and thus increases compression efficiency. Furthermore, noise is generally uncorrelated with sound sources. Therefore, removing the correlation helps to separate noise from sound sources. Also, sound sources are generally uncorrelated, and thus removing the correlation helps to identify the sound sources.
With cross-channel prediction, there is no preservation of signal energy. In approaches that use fixed matrixing (e.g., as used in CELT, Vorbis), there is no adaptation to signal characteristics. Approaches that use downmixing (e.g., as used in HE-AAC, MPEG Surround) are non-invertible. Additionally, Karhunen-Loève transform (KLT)/principle component analysis (PCA) (e.g., as used in MAACKLT3, PCA-based primary-ambience decomposition), when carried out in a conventional manner, is computationally difficult.